图像去模糊——DeblurGAN

精选原创

yerikyu 2023-11-14 10:33:28 博主文章分类：开发笔记 ©著作权

文章标签 生成器 ide tensorflow 文章分类 深度学习人工智能

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问题描述

图像去模糊一直是一个很重要的cv领域，尤其是在拍照和摄影。像去模糊是一种图像处理技术，旨在通过算法去除图像中的模糊或模糊效果。该技术可用于改善图像质量，使其更清晰、更易于识别和更具可视性。

模型选择

我选择了模型DeblurGAN实现，融合了二者的优点事半功倍！

DeblurGAN是一种基于生成对抗网络（GAN）的图像去模糊算法。该算法通过训练一个生成器网络和一个判别器网络来实现去模糊。生成器网络负责将模糊图像转换为清晰图像，而判别器网络则负责判断生成器网络输出的图像是否真实。

该项目中我用的模型如下：

图像去模糊——DeblurGAN_生成器

import tensorflow as tf
from tensorflow.keras import layers

# 定义生成器网络
def build_generator():
generator = tf.keras.Sequential()
generator.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))
generator.add(layers.BatchNormalization())
generator.add(layers.LeakyReLU())

generator.add(layers.Reshape((7, 7, 256)))
assert generator.output_shape == (None, 7, 7, 256)

generator.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
assert generator.output_shape == (None, 7, 7, 128)
generator.add(layers.BatchNormalization())
generator.add(layers.LeakyReLU())

generator.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
assert generator.output_shape == (None, 14, 14, 64)
generator.add(layers.BatchNormalization())
generator.add(layers.LeakyReLU())

generator.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
assert generator.output_shape == (None, 28, 28, 1)

return generator

# 定义判别器网络
def build_discriminator():
discriminator = tf.keras.Sequential()
discriminator.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
input_shape=[28, 28, 1]))
discriminator.add(layers.LeakyReLU())
discriminator.add(layers.Dropout(0.3))

discriminator.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
discriminator.add(layers.LeakyReLU())
discriminator.add(layers.Dropout(0.3))

discriminator.add(layers.Flatten())
discriminator.add(layers.Dense(1))

return discriminator

# 定义生成器和判别器的损失函数
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)

def generator_loss(fake_output):
return cross_entropy(tf.ones_like(fake_output), fake_output)

def discriminator_loss(real_output, fake_output):
real_loss = cross_entropy(tf.ones_like(real_output), real_output)
fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
total_loss = real_loss + fake_loss
return total_loss

# 定义生成器和判别器的优化器
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)

# 实例化生成器和判别器
generator = build_generator()
discriminator = build_discriminator()

# 定义训练循环
@tf.function
def train_step(images):
noise = tf.random.normal([BATCH_SIZE, 100])

with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator(noise, training=True)

real_output = discriminator(images, training=True)
fake_output = discriminator(generated_images, training=True)

gen_loss = generator_loss(fake_output)
disc_loss = discriminator_loss(real_output, fake_output)

gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)

generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

# 开始训练
def train(dataset, epochs):
for epoch in range(epochs):
for image_batch in dataset:
train_step(image_batch)

# 载入数据集并进行预处理
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5

BUFFER_SIZE = 60000